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R Bioinformatics Cookbook

You're reading from   R Bioinformatics Cookbook Utilize R packages for bioinformatics, genomics, data science, and machine learning

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781837634279
Length 396 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Dan MacLean Dan MacLean
Author Profile Icon Dan MacLean
Dan MacLean
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Setting Up Your R Bioinformatics Working Environment 2. Chapter 2: Loading, Tidying, and Cleaning Data in the tidyverse FREE CHAPTER 3. Chapter 3: ggplot2 and Extensions for Publication Quality Plots 4. Chapter 4: Using Quarto to Make Data-Rich Reports, Presentations, and Websites 5. Chapter 5: Easily Performing Statistical Tests Using Linear Models 6. Chapter 6: Performing Quantitative RNA-seq 7. Chapter 7: Finding Genetic Variants with HTS Data 8. Chapter 8: Searching Gene and Protein Sequences for Domains and Motifs 9. Chapter 9: Phylogenetic Analysis and Visualization 10. Chapter 10: Analyzing Gene Annotations 11. Chapter 11: Machine Learning with mlr3 12. Chapter 12: Functional Programming with purrr and base R 13. Chapter 13: Turbo-Charging Development in R with ChatGPT 14. Index 15. Other Books You May Enjoy

Getting ready

In this recipe, we’ll use a set of synthetic reads on the first 83 KB or so of the human genome chromosome 17. The reads were generated using the wgsim tool in samtools, an external command-line program. They have 64 single nucleotide polymorphisms (SNPs) introduced by wgsim, which can be seen in the snp_positions DataFrame that comes in rbioinfcookbook. We’ll use BAM and reference genome files that are stored in that package too, so we’ll need to install that along with the GenomicRanges, gmapR, rtracklayer, VariantAnnotation, and VariantTools Bioconductor packages, as well as the fs CRAN package.

How to do it…

Finding SNPs and insertions/deletions (INDELs) from sequence data using VariantTools can be done by performing the following steps:

  1. Import the required libraries:
    library(GenomicRanges)library(gmapR)library(rtracklayer)library(VariantAnnotation)library(VariantTools)
  2. Then, load the datasets:
    bam_file <- fs::path_package...
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